COVSMA

Table of Contents

• 1  COVSMA
• 2  COVSMA's first tool: COVSCO
  • 2.1  Data Exploration
    • 2.1.1  Introduction to the data: our X, our y
      • 2.1.1.1  The features: 22 variables from which we will predict our target
      • 2.1.1.2  The Target: New hospitalizations due to severe COVID19 cases
    • 2.1.2  New hospitalizations means over all departements in function of 7davg pollutants concentrations differencials
    • 2.1.3  New hospitalizations means over all departements in function of 1Mavg - Pollutants concentrations differencials
    • 2.1.4  New hospitalizations means over all departements in function of 1MMax Pollutants concentrations differencials
    • 2.1.5  Ozone (O3) and the number of severe COVID19 cases leading to hospitalization
      • 2.1.5.1  Departement 75: Paris region Ile de France
      • 2.1.5.2  Departement 83: Var region PACA
    • 2.1.6  Nitrogen dioxide (NO2) and the number of severe COVID19 cases leading to hospitalization
      • 2.1.6.1  Departement 75: Paris region Ile de France
      • 2.1.6.2  Departement 83: Var region PACA
    • 2.1.7  PM2.5 and the number of severe COVID19 cases leading to hospitalization
      • 2.1.7.1  Departement 75: Paris region Ile de France
      • 2.1.7.2  Departement 83: Var region PACA
    • 2.1.8  CO and the number of severe COVID19 cases leading to hospitalization
      • 2.1.8.1  Departement 75: Paris region Ile de France
    • 2.1.9  The most polluted departements of France
      • 2.1.9.1  The Ozone O3 Pollutant
      • 2.1.9.2  The PM2.5 Pollutant
      • 2.1.9.3  The NO2 Pollutant
      • 2.1.9.4  The CO Pollutant
      • 2.1.9.5  The PM10 Pollutant
    • 2.1.10  The facebook mobility index
      • 2.1.10.1  The facebook mobility index VS Ozone O3
      • 2.1.10.2  The facebook mobility index VS Carbon Monoxyde CO
      • 2.1.10.3  The facebook mobility index VS Nitrogen Dioxide NO2
      • 2.1.10.4  The facebook mobility index VS PM2.5
      • 2.1.10.5  The facebook mobility index VS PM10
  • 2.2  Training the model - Gradient Boosting for regression
    • 2.2.1  Hold-out and Cross Validation (MSE/MAE)
    • 2.2.2  Feature importance Report
    • 2.2.3  Exporting the model to a joblib file
    • 2.2.4  Running T-POT Auto ML optimization algorithm
    • 2.2.5  Recurrent Neural Network
  • 2.3  A classification of pollution levels, using the elbow method to get the optimal number of clusters
    • 2.3.1  The KMeans elbow method
    • 2.3.2  Finding minima in the kernel density estimation to identify splitting points, and describe the resulting ranges as (min,max) intervals
  • 2.4  Conclusion
    • 2.4.1  Design & Development:
    • 2.4.2  Verification of methodology
    • 2.4.3  Covid Risk of severe COVID19 cases due to PM2.5 forecast maps

COVSMA's first tool: COVSCO

COVSMA stands for Copernicus Satellites Versus Maladies: The current sanitary crisis generates the necessity to develop an online tool to monitor pollution levels, display alerts that will imply that governments automatically take measures: days without cars and trucks that are not 100% Electrical, monitor the live impact of the measures taken, forecast COVID19 risk due to pm2.5 exposure for up to 4 days in the future, and predict new hospitalisations due to severe COVID19 cases for all states/departements. We have named this tool with the analog name of COVSCO (Copernicus Satellites Versus COVID19). We start with France and its 96 departements. A follow up will be to apply the same methodology to severe respiratory diseases and to expand the model and databases to a global scale.

Data Exploration

Introduction to the data: our X, our y

/home/ludo915/anaconda3/envs/covsco/lib/python3.8/site-packages/tpot/builtins/__init__.py:36: UserWarning: Warning: optional dependency `torch` is not available. - skipping import of NN models.
  warnings.warn("Warning: optional dependency `torch` is not available. - skipping import of NN models.")

              nom  numero       time  hospi  reanim  newhospi  newreanim  \
34            Ain     1.0 2020-05-14  137.0     8.0       4.0        0.0   
35            Ain     1.0 2020-05-15  135.0     7.0       4.0        0.0   
36            Ain     1.0 2020-05-16  134.0     6.0       1.0        0.0   
37            Ain     1.0 2020-05-17  133.0     6.0       1.0        0.0   
38            Ain     1.0 2020-05-18  132.0     6.0       1.0        0.0   
...           ...     ...        ...    ...     ...       ...        ...   
34171  Val-d'Oise    95.0 2021-03-27  659.0    79.0      43.0        7.0   
34172  Val-d'Oise    95.0 2021-03-28  667.0    81.0      38.0        7.0   
34173  Val-d'Oise    95.0 2021-03-29  680.0    76.0      44.0        4.0   
34174  Val-d'Oise    95.0 2021-03-30  688.0    75.0      88.0        5.0   
34175  Val-d'Oise    95.0 2021-03-31  698.0    77.0      83.0        7.0   

        deces  gueris  dep_num  ...              Region_y  Departement_y  \
34       88.0   318.0      1.0  ...  Auvergne-Rhône-Alpes            Ain   
35       89.0   323.0      1.0  ...  Auvergne-Rhône-Alpes            Ain   
36       90.0   325.0      1.0  ...  Auvergne-Rhône-Alpes            Ain   
37       90.0   326.0      1.0  ...  Auvergne-Rhône-Alpes            Ain   
38       90.0   331.0      1.0  ...  Auvergne-Rhône-Alpes            Ain   
...       ...     ...      ...  ...                   ...            ...   
34171  1603.0  6857.0     95.0  ...         Île-de-France     Val-d'Oise   
34172  1606.0  6882.0     95.0  ...         Île-de-France     Val-d'Oise   
34173  1615.0  6902.0     95.0  ...         Île-de-France     Val-d'Oise   
34174  1632.0  6964.0     95.0  ...         Île-de-France     Val-d'Oise   
34175  1639.0  7024.0     95.0  ...         Île-de-France     Val-d'Oise   

      depnum_y Smokers  Nb_susp_501Y_V1  Nb_susp_501Y_V2_3  minority  \
34           1   0.262                0                  0   54821.0   
35           1   0.262                0                  0   54821.0   
36           1   0.262                0                  0   54821.0   
37           1   0.262                0                  0   54821.0   
38           1   0.262                0                  0   54821.0   
...        ...     ...              ...                ...       ...   
34171       95   0.213             9561                432  161947.0   
34172       95   0.213             9557                428  161947.0   
34173       95   0.213             9496                396  161947.0   
34174       95   0.213             8643                364  161947.0   
34175       95   0.213             7915                332  161947.0   

       pauvrete  rsa  ouvriers  
34         10.7  2.3     17.74  
35         10.7  2.3     17.74  
36         10.7  2.3     17.74  
37         10.7  2.3     17.74  
38         10.7  2.3     17.74  
...         ...  ...       ...  
34171      16.8  5.8     17.63  
34172      16.8  5.8     17.63  
34173      16.8  5.8     17.63  
34174      16.8  5.8     17.63  
34175      16.8  5.8     17.63  

[30912 rows x 92 columns]

	idx	pm25	no2	o3	pm10	co	pm257davg	no27davg	o37davg	co7davg	...	normno27davg	normo37davg	normpm107davg	normco7davg	normpm251Mavg	normno21Mavg	normo31Mavg	normpm101Mavg	normco1Mavg	newhospi
34	631877.0	6.403166	3.939003	43.262828	7.967417	177.622754	7.257504	3.476745	72.813923	161.817954	...	0.046602	0.586532	0.129071	0.234867	0.108579	0.048632	0.559299	0.094840	0.218359	4.0
35	631877.0	10.041256	4.039703	45.365958	12.985050	177.204810	7.283296	3.523846	71.713745	162.361518	...	0.047305	0.577448	0.127923	0.236187	0.119927	0.051150	0.496692	0.102941	0.226082	4.0
36	631877.0	8.650893	2.993409	64.447998	10.213892	173.986833	7.222348	3.502823	70.928693	162.525890	...	0.046991	0.570966	0.125429	0.236587	0.129652	0.051861	0.455272	0.105411	0.234060	1.0
37	631877.0	7.924968	2.470320	81.736362	11.228378	163.052671	7.159819	3.487527	70.520307	162.329540	...	0.046763	0.567594	0.123235	0.236110	0.127766	0.051226	0.458184	0.101608	0.239713	1.0
38	631877.0	8.803713	2.883282	79.918855	11.338718	186.330957	7.143561	3.477693	69.999498	162.848931	...	0.046616	0.563293	0.120713	0.237371	0.142927	0.049201	0.477197	0.113024	0.255846	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
34171	1215390.0	6.100140	6.627001	70.229467	11.613934	185.482428	14.580851	14.493739	56.915039	207.123258	...	0.211142	0.455257	0.281847	0.344925	0.302061	0.237179	0.471712	0.271828	0.352307	43.0
34172	1215390.0	8.425896	9.099569	68.209629	13.743489	193.139682	14.195306	14.223872	57.801110	206.960739	...	0.207112	0.462573	0.269762	0.344530	0.289930	0.240049	0.480526	0.265162	0.358714	38.0
34173	1215390.0	13.033086	29.526526	54.441418	18.517216	237.388093	14.249303	14.775230	57.704602	207.544318	...	0.215346	0.461776	0.270202	0.345948	0.295797	0.280570	0.463981	0.266083	0.371198	44.0
34174	1215390.0	21.792762	51.437356	40.131381	30.011262	307.382279	14.525749	16.057324	57.291931	210.476992	...	0.234495	0.458369	0.274637	0.353072	0.286940	0.319473	0.461757	0.261254	0.379313	88.0
34175	1215390.0	21.862512	49.729555	42.796275	32.744934	290.497534	14.654314	17.360417	57.019812	213.319222	...	0.253957	0.456122	0.278918	0.359977	0.278900	0.366677	0.452311	0.260245	0.393208	83.0

30912 rows × 56 columns

The features: 22 variables from which we will predict our target

Index(['idx', 'pm25', 'pm257davg', 'normpm25', 'hospiprevday',
       'covidpostestprevday', 'prevdaytotalcovidcasescumulated',
       'all_day_bing_tiles_visited_relative_change',
       'all_day_ratio_single_tile_users', 'vac1nb', 'vac2nb',
       'Insuffisance respiratoire chronique grave (ALD14)',
       'Insuffisance cardiaque grave, troubles du rythme graves, cardiopathies valvulaires graves, cardiopathies congénitales graves (ALD5)',
       'Smokers', 'minority', 'Nb_susp_501Y_V1', 'Nb_susp_501Y_V2_3',
       '1MMaxpm25', 'pm251Mavg', 'pauvrete', 'rsa', 'ouvriers'],
      dtype='object')
22

The Target: New hospitalizations due to severe COVID19 cases

The daily number of new hospitalizations due to severe COVID19 cases for every French departement is what we will predict.

New hospitalizations means over all departements in function of 7davg pollutants concentrations differencials

Text(0, 0.5, 'newhospimean')

New hospitalizations means over all departements in function of 1Mavg - Pollutants concentrations differencials

Text(0, 0.5, 'newhospimean')

New hospitalizations means over all departements in function of 1MMax Pollutants concentrations differencials

Text(0, 0.5, 'newhospimean')

Ozone (O3) and the number of severe COVID19 cases leading to hospitalization

Departement 75: Paris region Ile de France

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Departement 83: Var region PACA

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Nitrogen dioxide (NO2) and the number of severe COVID19 cases leading to hospitalization

Departement 75: Paris region Ile de France

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Departement 83: Var region PACA

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PM2.5 and the number of severe COVID19 cases leading to hospitalization

Departement 75: Paris region Ile de France

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Departement 83: Var region PACA

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CO and the number of severe COVID19 cases leading to hospitalization

Departement 75: Paris region Ile de France

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The most polluted departements of France

The Ozone O3 Pollutant

[95.]
                Département  Numéro Date of pollution peak     1MMaxo3  \
0                Val-d'Oise    95.0             2020-08-09  122.889769   
1   Alpes-de-Haute-Provence     4.0             2020-08-08  119.833327   
2                 Haut-Rhin    68.0             2020-07-31  116.979563   
3                   Moselle    57.0             2020-08-10  115.873423   
4                       Ain     1.0             2020-09-18  114.506434   
..                      ...     ...                    ...         ...   
90     Pyrénées-Atlantiques    64.0             2020-08-06   94.093549   
91                     Gard    30.0             2020-05-20   93.974202   
92                     Cher    18.0             2020-07-13   93.921768   
93          Hautes-Pyrénées    65.0             2020-05-30   93.086194   
94                   Ariège     9.0             2020-05-29   90.297432   

    totalcovidcasescumulated  Population Index  
0                     237793         1215390.0  
1                      20858          161799.0  
2                      79025          762607.0  
3                     141705         1044486.0  
4                     109223          631877.0  
..                       ...               ...  
90                     62382          670032.0  
91                    107351          738189.0  
92                     32904          308992.0  
93                     25307          228582.0  
94                     14118          152499.0  

[95 rows x 6 columns]

The PM2.5 Pollutant

[59.]
                Département  Numéro Date of pollution peak  1MMaxpm25  \
0                      Nord    59.0             2020-11-27  39.932960   
1                 Haut-Rhin    68.0             2021-02-24  37.243984   
2               Deux-Sèvres    79.0             2021-03-09  36.380767   
3                     Paris    75.0             2021-01-02  35.418335   
4                    Vienne    86.0             2021-03-09  34.895373   
..                      ...     ...                    ...        ...   
90          Alpes-Maritimes     6.0             2021-03-06  20.296409   
91                Côte-d'Or    21.0             2021-02-23  20.115372   
92                   Lozère    48.0             2021-03-04  19.935369   
93  Alpes-de-Haute-Provence     4.0             2021-02-23  19.765781   
94                  Ardèche     7.0             2021-03-04  19.407017   

    totalcovidcasescumulated  Population Index  
0                     477936         2605238.0  
1                      79025          762607.0  
2                      35767          374435.0  
3                     404122         2206488.0  
4                      38030          434887.0  
..                       ...               ...  
90                    219293         1082440.0  
91                     66868          533147.0  
92                     10367           76309.0  
93                     20858          161799.0  
94                     45658          324209.0  

[95 rows x 6 columns]

The NO2 Pollutant

[75.]
            Département  Numéro Date of pollution peak   1MMaxno2  \
0                 Paris    75.0             2021-03-31  67.312539   
1        Hauts-de-Seine    92.0             2021-03-02  64.475306   
2          Val-de-Marne    94.0             2021-01-08  53.384538   
3            Val-d'Oise    95.0             2021-03-02  51.599818   
4              Yvelines    78.0             2020-11-26  43.024886   
..                  ...     ...                    ...        ...   
90               Lozère    48.0             2021-01-07   7.727260   
91         Corse-du-Sud   201.0             2020-12-09   6.285247   
92               Ariège     9.0             2021-01-09   6.254226   
93  Pyrénées-Orientales    66.0             2021-01-09   6.071631   
94          Haute-Corse   202.0             2021-01-09   5.592579   

    totalcovidcasescumulated  Population Index  
0                     404122         2206488.0  
1                     266670         1601569.0  
2                     265897         1372389.0  
3                     237793         1215390.0  
4                     212766         1427291.0  
..                       ...               ...  
90                     10367           76309.0  
91                     11183          152730.0  
92                     14118          152499.0  
93                     45028          471038.0  
94                     13884          174553.0  

[95 rows x 6 columns]

The CO Pollutant

[92.]
            Département  Numéro Date of pollution peak     1MMaxco  \
0        Hauts-de-Seine    92.0             2020-11-26  476.783872   
1              Bas-Rhin    67.0             2020-11-10  442.772829   
2                 Paris    75.0             2020-11-26  442.031472   
3          Val-de-Marne    94.0             2021-01-02  400.354289   
4      Bouches-du-Rhône    13.0             2021-02-24  364.207868   
..                  ...     ...                    ...         ...   
90               Cantal    15.0             2021-01-06  204.699850   
91               Lozère    48.0             2021-01-07  202.482931   
92      Hautes-Pyrénées    65.0             2021-01-10  200.001928   
93               Ariège     9.0             2021-01-11  189.451920   
94  Pyrénées-Orientales    66.0             2021-03-06  182.390438   

    totalcovidcasescumulated  Population Index  
0                     266670         1601569.0  
1                     138675         1116658.0  
2                     404122         2206488.0  
3                     265897         1372389.0  
4                     404460         2016622.0  
..                       ...               ...  
90                     11762          146219.0  
91                     10367           76309.0  
92                     25307          228582.0  
93                     14118          152499.0  
94                     45028          471038.0  

[95 rows x 6 columns]

The PM10 Pollutant

[67.]
     Département  Numéro Date of pollution peak  1MMaxpm10  \
0       Bas-Rhin    67.0             2021-02-25  74.188288   
1      Haut-Rhin    68.0             2021-02-25  71.831104   
2   Corse-du-Sud   201.0             2021-02-06  70.996064   
3         Vosges    88.0             2021-02-25  70.504318   
4    Haute-Saône    70.0             2021-02-25  69.817902   
..           ...     ...                    ...        ...   
90       Mayenne    53.0             2021-03-03  40.262764   
91          Eure    27.0             2021-03-03  39.774691   
92      Calvados    14.0             2021-03-02  38.762629   
93        Sarthe    72.0             2021-03-03  36.942586   
94          Orne    61.0             2021-03-02  36.142811   

    totalcovidcasescumulated  Population Index  
0                     138675         1116658.0  
1                      79025          762607.0  
2                      11183          152730.0  
3                      41055          372016.0  
4                      28170          237706.0  
..                       ...               ...  
90                     29192          307940.0  
91                     65397          601948.0  
92                     61873          693579.0  
93                     57288          568445.0  
94                     29094          286618.0  

[95 rows x 6 columns]

The facebook mobility index

The facebook mobility index VS Ozone O3

<AxesSubplot:>

The facebook mobility index VS Carbon Monoxyde CO

<AxesSubplot:>

The facebook mobility index VS Nitrogen Dioxide NO2

<AxesSubplot:>

The facebook mobility index VS PM2.5

<AxesSubplot:>

The facebook mobility index VS PM10

<AxesSubplot:>

Training the model - Gradient Boosting for regression

Gradient Boosting for regression.

GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.

Stack of estimators with a final regressor.

Stacked generalization consists in stacking the output of individual estimator and use a regressor to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.

Note that estimators_ are fitted on the full X while finalestimator is trained using cross-validated predictions of the base estimators using cross_val_predict.

Hold-out and Cross Validation (MSE/MAE)

T-Pot exported current best pipeline
MSE:
51.92040608159389
MAE:
3.572805773399538


(30912, 1)
(30912, 92)

Feature importance Report

Scikit Learn - GradientBoostingRegressor:
                                                index  feature_importance
9                                              vac1nb            0.001813
16                                  Nb_susp_501Y_V2_3            0.001920
19                                           pauvrete            0.002501
13                                            Smokers            0.002902
10                                             vac2nb            0.002929
1                                                pm25            0.002964
20                                                rsa            0.003047
3                                            normpm25            0.003323
21                                           ouvriers            0.003725
11  Insuffisance respiratoire chronique grave (ALD14)            0.003786
15                                    Nb_susp_501Y_V1            0.004309
12  Insuffisance cardiaque grave, troubles du ryth...            0.004505
14                                           minority            0.004867
0                                                 idx            0.005109
17                                          1MMaxpm25            0.006621
18                                          pm251Mavg            0.007130
2                                           pm257davg            0.009068
7          all_day_bing_tiles_visited_relative_change            0.011923
8                     all_day_ratio_single_tile_users            0.039006
6                     prevdaytotalcovidcasescumulated            0.122313
5                                 covidpostestprevday            0.274968
4                                        hospiprevday            0.481272

<Figure size 900x600 with 0 Axes>

Exporting the model to a joblib file

Running T-POT Auto ML optimization algorithm

TPOTRegressor

Version 0.11.6.post1 of tpot is outdated. Version 0.11.7 was released Wednesday January 06, 2021.

TPOT closed during evaluation in one generation.
WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.


TPOT closed prematurely. Will use the current best pipeline.
Best pipeline: ExtraTreesRegressor(CombineDFs(input_matrix, input_matrix), bootstrap=False, max_features=0.5, min_samples_leaf=1, min_samples_split=20, n_estimators=100)
-48.56940976377202

Recurrent Neural Network

A classification of pollution levels, using the elbow method to get the optimal number of clusters

The KMeans elbow method

The elbow method determines that the optimal number of clusters for PM2.5 Levels is k = 4

Finding minima in the kernel density estimation to identify splitting points, and describe the resulting ranges as (min,max) intervals

0.6375679675824064
39.9329597660099
[0.6375679675824064, 10.46141591718928, 20.285263866796154, 30.10911181640303, 39.9329597660099]


                  nom pm25levelstring
4983             Nord            High
5695    Pas-de-Calais            High
6051            Somme            High
31683           Paris            High
33107  Hauts-de-Seine            High
34175      Val-d'Oise            High
OK

Conclusion

<seaborn.axisgrid.PairGrid at 0x7fb105e6f2b0>

Design & Development:

• Part of our product is dedicated to predicting the daily new derpartmental number of hospitalizations due to severe cases of COVID19, these predictions are made by a state of the art Machine Learning model, fine tuned by an Auto Machine Learning optimizer, and are necessary for hospitals and clinics to potentially organize emergency outflows of patients to other locations in case of over-crowding.

• The virus' contagious characteristic and facebook's mobility index lead our model's feature importance report but data visualizationg makes it clear to the eye that the mean of all french departments' new hospitalizations due to severe COVID-19 cases is an increasing function of PM2.5-1-M Maximum and PM10 7-day average differentials. Furthermore, unusually high levels in Ozone at ground level seem to act as a trigger to the epidemy.

• Our model makes predictions based on live data flows, composed of a set of 22 features, some such as ground level atmospheric pollutant concentrations, others reflecting prevalence/incidence of variants, others the impact of the vaccination campaign and soon temperature and humidity data.

• Our algorithm is first trained on a maximum depth historical database obtained by merging features streamed from a multitude of data sources/providers in our database. The training of the model will frequently be launched with the goal that the algorithm continues learning from new data. An API will load the latest model and feed directly all the necessary outputs, including the integration of GIS elements, to the company's product website.

• Our tool also ranks French departments by their pollution levels and gives alerts when PM2.5, PM10 or/and other pollutants levels are abnormally high, determining an optimal number of levels is with the K-Means clustering elbow method. These alerts are translated into recommendations with the goal that the government automatically takes measures to stop heavy traffic pollution coming from non electrical cars and trucks until the monitoring team determines that the live levels in pollutants have lowered to a safe cluster interval.

Verification of methodology

• Automated Error reports comparing ground truth to predictions keep track of a history of MSEs and MAEs, this will allow the monitoring team to day after day check the evolution of our predictions' precision. We have cross-validated our results on a dataset of 30912 data entries and we obtain a MAE of approximately 4 new daily hospitalizations per department.

Covid Risk of severe COVID19 cases due to PM2.5 forecast maps

Data pre-processing:
Population ... OK
Covid ...      numero       hospi     reanim   newhospi  newreanim       deces  \
0       1.0  191.000000  21.428571  12.571429   2.857143  574.142857   
1       2.0  312.285714  54.857143  22.285714   3.571429  944.142857   
2       3.0   90.000000  17.428571   8.571429   1.000000  523.142857   
3       4.0  150.142857   6.285714   6.571429   0.428571  229.000000   
4       5.0  120.571429  12.428571   5.000000   0.714286  237.285714   
..      ...         ...        ...        ...        ...         ...   
96    971.0  100.428571  22.142857  10.714286   2.428571  213.142857   
97    972.0  113.857143  25.428571  11.142857   3.714286   65.714286   
98    973.0   41.428571  11.857143   5.571429   1.285714   91.000000   
99    974.0  149.571429  39.000000  10.142857   3.000000  146.142857   
100   976.0   13.285714   6.000000   0.000000   0.142857  124.000000   

          gueris  
0    2454.142857  
1    3483.285714  
2    1824.714286  
3     956.142857  
4    1042.428571  
..           ...  
96    982.857143  
97    540.571429  
98   2295.000000  
99   1307.571429  
100  1225.428571  

[101 rows x 7 columns]
       reg  dep  com article                     com_nom       lon        lat  \
0       11   75   56     NaN                       PARIS  2.352222  48.856614   
1       11   77    1     NaN            ACHERES-LA-FORET  2.570289  48.354976   
2       11   77   10     NaN  AUBEPIERRE-OZOUER-LE-REPOS  2.890552  48.632323   
3       11   77  100      LE            CHATELET-EN-BRIE  2.792095  48.504945   
4       11   77  101     NaN          CHATENAY-SUR-SEINE  3.096229  48.418774   
...    ...  ...  ...     ...                         ...       ...        ...   
36313   94  202    9     NaN                      ALERIA  9.512429  42.104248   
36314   94  202   93     NaN                     CORBARA  8.907482  42.615508   
36315   94  202   95     NaN                     CORSCIA  9.042592  42.354646   
36316   94  202   96     NaN                       CORTE  9.149022  42.309409   
36317   94  202   97     NaN                       COSTA  9.001945  42.574916   

              total       idx               hospi  
0      2.240213e+06         1   1717.142857142857  
1      1.285127e+03         1   714.2857142857143  
2      8.993365e+02         1   714.2857142857143  
3      4.454928e+03         1   714.2857142857143  
4      8.795762e+02         1   714.2857142857143  
...             ...       ...                 ...  
36313  2.005203e+03  0.134286  26.857142857142858  
36314  1.002777e+03  0.134286  26.857142857142858  
36315  1.833197e+02  0.134286  26.857142857142858  
36316  6.756341e+03  0.134286  26.857142857142858  
36317  6.962550e+01  0.134286  26.857142857142858  

[36318 rows x 10 columns]
OK
PM2.5 ... OK
<xarray.DataArray 'pm2p5_conc' (com: 36318)>
array([15.13080737, 11.59004665, 13.34271505, ...,  4.27186682,
        4.55498089,  4.32766635])
Coordinates:
    time       object ...
    longitude  (com) float64 2.352 2.57 2.891 2.792 ... 8.907 9.043 9.149 9.002
    latitude   (com) float64 48.86 48.35 48.63 48.5 ... 42.62 42.35 42.31 42.57
  * com        (com) int64 0 1 2 3 4 5 6 ... 36312 36313 36314 36315 36316 36317
2.473360196194873

<ipython-input-38-8e33d2a1f13e>:216: DeprecationWarning: The background_patch property is deprecated. Use GeoAxes.patch instead.
  ax1.background_patch.set_fill(False)
<ipython-input-38-8e33d2a1f13e>:222: DeprecationWarning: The outline_patch property is deprecated. Use GeoAxes.spines['geo'] or the default Axes properties instead.
  a.outline_patch.set_linewidth(0.)

<ipython-input-160-c7a7d55f1956>:18: DeprecationWarning: The background_patch property is deprecated. Use GeoAxes.patch instead.
  ax.background_patch.set_fill(False)
<ipython-input-160-c7a7d55f1956>:23: DeprecationWarning: The outline_patch property is deprecated. Use GeoAxes.spines['geo'] or the default Axes properties instead.
  ax.outline_patch.set_linewidth(0.)

<matplotlib.axes._subplots.AxesSubplot at 0x7ff5c35a0490>